The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform
Michael Baldea - One of the best experts on this subject based on the ideXlab platform.
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stochastic scheduling and control using data driven nonlinear dynamic models application to demand Response Operation of a chlor alkali plant
Industrial & Engineering Chemistry Research, 2020Co-Authors: Jodie M Simkoff, Michael BaldeaAbstract:Modern chemical manufacturers are increasingly interested in improving the agility of their Operations. This is particularly true in regions with deregulated electricity markets, where participatio...
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demand Response Operation of electricity intensive chemical processes for reduced greenhouse gas emissions application to an air separation unit
ACS Sustainable Chemistry & Engineering, 2019Co-Authors: Morgan T Kelley, Ross Baldick, Michael BaldeaAbstract:A recent push for reduced greenhouse gas (GHG) emissions has led (in part) to the addition of renewable electricity generation sources to the power generation mix. Renewables such as wind and solar are desynchronized from grid demand, requiring the use of fossil fuels to bridge the gap. We propose a novel production scheduling formulation, emissions-minimizing production (EMP), which utilizes time-based information on the nature of the power generation mix to lower GHG emissions related to the transmission and generation of electricity for industrial users. We demonstrate the application of EMP on a single-column air separation unit. The scheduling problem is cast as a mixed integer linear program that can be solved in a practical amount of time. Extensive numerical studies are used to place EMP in the context of other production scheduling methods (such as demand Response), and demonstrate its potential for significant reductions in GHG emissions.
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An MILP framework for optimizing demand Response Operation of air separation units
Applied Energy, 2018Co-Authors: Morgan T Kelley, Ross Baldick, Richard C. Pattison, Michael BaldeaAbstract:Abstract Peaks in renewable electricity generation and consumer demand are desynchronized in time, posing a challenge for grid operators. Industrial demand Response (DR) has emerged as a candidate for mitigating this variability. In this paper, we demonstrate the application of DR to an air separation unit (ASU). We develop a novel optimal production scheduling framework that accounts for day-ahead electricity prices to modulate the grid load presented by the plant. We account for the dynamics of the plant using a novel dynamic modeling strategy, which allows us to formulate the corresponding optimization problem as a mixed integer linear program (MILP). Further, we present a new relaxation scheme that affords fast solutions of this MILP. Extensive simulation results show significant reductions in operating costs (that benefit the plant) and reductions in peak power demand (that benefit the grid).
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optimization based assessment of design limitations to air separation plant agility in demand Response scenarios
Journal of Process Control, 2015Co-Authors: Yanan Cao, Michael Baldea, Christopher L E Swartz, Stephane BlouinAbstract:Abstract The significant effect that the design of a plant can have on its dynamic performance has led to methodologies for systematic analysis of the interaction between design and control, and for inclusion of dynamic performance considerations in plant design. This article focuses on the assessment of design limitations to the agility of a nitrogen plant in Response to demand and electricity price fluctuations. A two-tiered approach is proposed, where an economics-based optimization problem is first solved to determine the optimal steady-state operating point, after which a dynamic optimization problem is solved to minimize a measure of the transition time to the new operating point. Design limitations to the plant's responsiveness may be inferred by analysis of the active constraints. The approach is demonstrated on a comprehensive case study based on an existing industrial nitrogen plant. The design limitations of the existing plant configuration are identified, and the potential benefits of selected design modifications to demand Response Operation are assessed.
Franck Mars - One of the best experts on this subject based on the ideXlab platform.
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A pilot study on the dynamics of online risk assessment by the passenger of a self-driving car among pedestrians
2020Co-Authors: Jeffery Petit, Camilo Charron, Franck MarsAbstract:In autonomous cars, the automation systems assume complete Operational control. In this situation, it is essential that passengers always feel comfort-able with the vehicle's decisions. In this project, we are specifically inter-ested in risk assessment by the passenger of an autonomous car navigating among pedestrians in a shared space. A driving simulator experiment was conducted with 27 participants. The challenge was twofold: on the one hand, to find a link between the pedestrians' avoidance behavior of the ve-hicle and the risk felt by the passenger; and on the other hand, to try to pre-dict this perceived risk in real time. The study revealed a significant effect of two factors on the risk assessed by the participants: (1) the value of the TTC at the moment the vehicle begins a pedestrian avoidance maneuver; (2) the lateral distance it leaves to the pedestrian. The proposed real-time pre-diction model is based on the principle of impulse Response Operation. This new paradigm assumes that the passenger's risk assessment is the result of a quantifiable unconscious internal phenomenon that has been estimated us-ing the dynamics of the perceived pedestrian approach. The results showed that this approach was predictive of risk for isolated avoidance maneuvers, but was insufficient to explain the variability in the risk assessment behav-ior of the participants.
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HCI (32) - A pilot study on the dynamics of online risk assessment by the passenger of a self-driving car among pedestrians
Lecture Notes in Computer Science, 2020Co-Authors: Jeffery Petit, Camilo Charron, Franck MarsAbstract:In autonomous cars, the automation systems assume complete Operational control. In this situation, it is essential that passengers always feel comfortable with the vehicle’s decisions. In this project, we are specifically interested in risk assessment by the passenger of an autonomous car navigating among pedestrians in a shared space. A driving simulator experiment was conducted with 27 participants. The challenge was twofold: on the one hand, to find a link between the pedestrians’ avoidance behavior of the vehicle and the risk felt by the passenger; and on the other hand, to try to predict this perceived risk in real time. The study revealed a significant effect of two factors on the risk assessed by the participants: (1) the value of the TTC at the moment the vehicle begins a pedestrian avoidance maneuver; (2) the lateral distance it leaves to the pedestrian. The proposed real-time prediction model is based on the principle of impulse Response Operation. This new paradigm assumes that the passenger’s risk assessment is the result of a quantifiable unconscious internal phenomenon that has been estimated using the dynamics of the perceived pedestrian approach. The results showed that this approach was predictive of risk for isolated avoidance maneuvers, but was insufficient to explain the variability in the risk assessment behavior of the participants.
Jeffery Petit - One of the best experts on this subject based on the ideXlab platform.
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A pilot study on the dynamics of online risk assessment by the passenger of a self-driving car among pedestrians
2020Co-Authors: Jeffery Petit, Camilo Charron, Franck MarsAbstract:In autonomous cars, the automation systems assume complete Operational control. In this situation, it is essential that passengers always feel comfort-able with the vehicle's decisions. In this project, we are specifically inter-ested in risk assessment by the passenger of an autonomous car navigating among pedestrians in a shared space. A driving simulator experiment was conducted with 27 participants. The challenge was twofold: on the one hand, to find a link between the pedestrians' avoidance behavior of the ve-hicle and the risk felt by the passenger; and on the other hand, to try to pre-dict this perceived risk in real time. The study revealed a significant effect of two factors on the risk assessed by the participants: (1) the value of the TTC at the moment the vehicle begins a pedestrian avoidance maneuver; (2) the lateral distance it leaves to the pedestrian. The proposed real-time pre-diction model is based on the principle of impulse Response Operation. This new paradigm assumes that the passenger's risk assessment is the result of a quantifiable unconscious internal phenomenon that has been estimated us-ing the dynamics of the perceived pedestrian approach. The results showed that this approach was predictive of risk for isolated avoidance maneuvers, but was insufficient to explain the variability in the risk assessment behav-ior of the participants.
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HCI (32) - A pilot study on the dynamics of online risk assessment by the passenger of a self-driving car among pedestrians
Lecture Notes in Computer Science, 2020Co-Authors: Jeffery Petit, Camilo Charron, Franck MarsAbstract:In autonomous cars, the automation systems assume complete Operational control. In this situation, it is essential that passengers always feel comfortable with the vehicle’s decisions. In this project, we are specifically interested in risk assessment by the passenger of an autonomous car navigating among pedestrians in a shared space. A driving simulator experiment was conducted with 27 participants. The challenge was twofold: on the one hand, to find a link between the pedestrians’ avoidance behavior of the vehicle and the risk felt by the passenger; and on the other hand, to try to predict this perceived risk in real time. The study revealed a significant effect of two factors on the risk assessed by the participants: (1) the value of the TTC at the moment the vehicle begins a pedestrian avoidance maneuver; (2) the lateral distance it leaves to the pedestrian. The proposed real-time prediction model is based on the principle of impulse Response Operation. This new paradigm assumes that the passenger’s risk assessment is the result of a quantifiable unconscious internal phenomenon that has been estimated using the dynamics of the perceived pedestrian approach. The results showed that this approach was predictive of risk for isolated avoidance maneuvers, but was insufficient to explain the variability in the risk assessment behavior of the participants.
Morgan T Kelley - One of the best experts on this subject based on the ideXlab platform.
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demand Response Operation of electricity intensive chemical processes for reduced greenhouse gas emissions application to an air separation unit
ACS Sustainable Chemistry & Engineering, 2019Co-Authors: Morgan T Kelley, Ross Baldick, Michael BaldeaAbstract:A recent push for reduced greenhouse gas (GHG) emissions has led (in part) to the addition of renewable electricity generation sources to the power generation mix. Renewables such as wind and solar are desynchronized from grid demand, requiring the use of fossil fuels to bridge the gap. We propose a novel production scheduling formulation, emissions-minimizing production (EMP), which utilizes time-based information on the nature of the power generation mix to lower GHG emissions related to the transmission and generation of electricity for industrial users. We demonstrate the application of EMP on a single-column air separation unit. The scheduling problem is cast as a mixed integer linear program that can be solved in a practical amount of time. Extensive numerical studies are used to place EMP in the context of other production scheduling methods (such as demand Response), and demonstrate its potential for significant reductions in GHG emissions.
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An MILP framework for optimizing demand Response Operation of air separation units
Applied Energy, 2018Co-Authors: Morgan T Kelley, Ross Baldick, Richard C. Pattison, Michael BaldeaAbstract:Abstract Peaks in renewable electricity generation and consumer demand are desynchronized in time, posing a challenge for grid operators. Industrial demand Response (DR) has emerged as a candidate for mitigating this variability. In this paper, we demonstrate the application of DR to an air separation unit (ASU). We develop a novel optimal production scheduling framework that accounts for day-ahead electricity prices to modulate the grid load presented by the plant. We account for the dynamics of the plant using a novel dynamic modeling strategy, which allows us to formulate the corresponding optimization problem as a mixed integer linear program (MILP). Further, we present a new relaxation scheme that affords fast solutions of this MILP. Extensive simulation results show significant reductions in operating costs (that benefit the plant) and reductions in peak power demand (that benefit the grid).
Chien-hsin Chang - One of the best experts on this subject based on the ideXlab platform.
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near real time mapping of peak ground acceleration and peak ground velocity following a strong earthquake
Bulletin of the Seismological Society of America, 2004Co-Authors: Yih-min Wu, Tzay-chyn Shin, Chien-hsin ChangAbstract:During a disastrous earthquake, the early assessment and timely reporting of the peak ground acceleration (PGA) and peak ground velocity (PGV) maps will be crucial in an effective emergency Response Operation. In this study, we first derive an empirical relationship between M L and M W. The PGA and PGV attenuation relationships are deduced with data from the Taiwan Strong Motion Instrumentation Program (TSMIP) and the Taiwan Rapid Earthquake Information Release System (TREIRS). Site corrections of the attenuation relationships for shallow and large earthquakes in Taiwan region are also obtained. Peak values of earthquake strong ground motion can be well determined in Taiwan as soon as the earthquake location is determined, and magnitudes are calculated by the TREIRS. This peak ground motion value information can be immediately turned into the calculated PGA and PGV maps that can be issued within two minutes of the earthquake origin time. During any disastrous earthquake, these maps are found to be very useful for immediate seismic damage assessment and dispatching of emergency Response missions. Manuscript received 1 December 2000.
